Partnerships to promote school to work transitions : evidence from the field

Internationally, vocational education and training (VET) is challenged by increasing skills shortages in certain industries and rapidly changing skill requirements. Rigid and centralised state bureaucracies have proven inadequate to adapt to these challenges. Increasingly, partnerships between schools and industry have been established as a potential strategy to address local labour market demand and to provide school to work transition programs. Drawing on experiences in Australia, this paper reports on a case study of government-let partnerships between schools and industry. The Queensland Gateway schools initiative currently involves over 120 schools. The study aimed to understand how partnerships were constructed in this initiative. Selected partnerships were analysed in terms of the following principles of public-private partnerships – efficiency, effectiveness, sustainability, and beneficiaries. Although there are some benefits of partnership activities reported by both school and industry stakeholders, little evidence was found that the above underlying principles had been addressed to a significant extent in the Gateway school initiative. Further, these partnerships are often tenuously facilitated by individuals who have limited infrastructure or strategic support. Implications are that project stakeholders have not sufficiently accommodated theoretical perspectives on implementation and management of partnerships. Similar initiatives may be improved if stakeholders are cognisant of the underlying principles supporting successful public-private partnerships.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Purpose: Flat-detector, cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. Methods: The rich sources of prior information in IGRT are incorporated into a hidden Markov random field (MRF) model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk (OAR). The voxel labels are estimated using the iterated conditional modes (ICM) algorithm. Results: The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom (CIRS, Inc. model 062). The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. Conclusions: By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.